A biologically-inspired distributed resilient flocking control for multi-agent system with uncertain dynamics and unknown disturbances

In this paper, the problem of flocking of MultiAgent Systems (MAS) in presence of system uncertainties and unknown disturbances is investigated. A biologically-inspired novel distributed resilient controller based on a computational model of emotional learning in mammalian brain is proposed. The methodology, known as Brain Emotional Learning Based Intelligent Controller (BELBIC), embeds a resilience mechanism with multi-objective properties into the flocking control strategy in a distributed fashion. The new strategy combines the learning capabilities of BELBIC with the flocking, and makes it a very promising approach, especially while dealing with system uncertainties and/or unknown disturbances. In addition, the low computational complexity of the proposed method makes it very suitable for practical implementation in real-time applications. Eventually, the effectiveness of the proposed intelligent resilient flocking control approach is demonstrated for flocking control of MAS.

[1]  Saeed Bagheri Shouraki,et al.  Attitude control of a Quadrotor using Brain Emotional Learning Based Intelligent Controller , 2013, 2013 13th Iranian Conference on Fuzzy Systems (IFSC).

[2]  Yongduan Song,et al.  Fully distributed flocking with a moving leader for Lagrange networks with parametric uncertainties , 2015, Autom..

[3]  Christian Balkenius,et al.  EMOTIONAL LEARNING: A COMPUTATIONAL MODEL OF THE AMYGDALA , 2001, Cybern. Syst..

[4]  Alvaro Vargas-Clara,et al.  Unmanned Ground Vehicle Navigation Using Brain Emotional Learning Based Intelligent Controller (BELBIC) , 2015 .

[5]  Siti Zaiton Mohd Hashim,et al.  A review of emotional learning and it's utilization in control engineering , 2010, SOCO 2010.

[6]  Reza Olfati-Saber,et al.  Flocking for multi-agent dynamic systems: algorithms and theory , 2006, IEEE Transactions on Automatic Control.

[7]  Patricio Ordaz,et al.  Implementation of leader-follower linear consensus algorithm for coordination of multiple aircrafts , 2015, 2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS).

[8]  Mohammad Jafari.  On the Cooperative Control and Obstacle Avoidance of MultiVehicle Systems , 2015 .

[9]  Seyyed Hamid Elyas,et al.  Optimal tuning of Brain Emotional Learning Based Intelligent Controller using Clonal Selection Algorithm , 2013, ICCKE 2013.

[10]  Hao Xu,et al.  Distributed near optimal flocking control for multiple Unmanned Aircraft Systems , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[11]  Shamik Sengupta,et al.  Adaptive Flocking Control of Multiple Unmanned Ground Vehicles by Using a UAV , 2015, ISVC.

[12]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1998 .

[13]  Wansheng Tang,et al.  Flocking of Multi‐Agents Following a Leader with Adaptive Protocol in a Noisy Environment , 2014 .

[14]  Caro Lucas,et al.  Introducing Belbic: Brain Emotional Learning Based Intelligent Controller , 2004, Intell. Autom. Soft Comput..

[15]  Gang Sun,et al.  Neural adaptive control for leader–follower flocking of networked nonholonomic agents with unknown nonlinear dynamics , 2014 .

[16]  Tansel Yucelen,et al.  Adaptive architectures for resilient control of networked multiagent systems in the presence of misbehaving agents , 2018, Int. J. Control.

[17]  Babak Nadjar Araabi,et al.  Implementation of Emotional Controller for Interior Permanent Magnet Synchronous Motor Drive , 2006, Conference Record of the 2006 IEEE Industry Applications Conference Forty-First IAS Annual Meeting.

[18]  J. Morén,et al.  A computational model of emotional learning in the amygdala. , 2000 .

[19]  Wenwu Yu,et al.  An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination , 2012, IEEE Transactions on Industrial Informatics.

[20]  Naira Hovakimyan,et al.  Distributed multi-agent tracking and estimation with uncertain agent dynamics , 2011, Proceedings of the 2011 American Control Conference.

[21]  Farid Najafi,et al.  Fuzzy tuning of Brain Emotional Learning Based Intelligent Controllers , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[22]  Hao Xu,et al.  Brain Emotional Learning-Based Intelligent Controller for flocking of Multi-Agent Systems , 2017, 2017 American Control Conference (ACC).

[23]  R. Mirheidari,et al.  A new Lyapunov based algorithm for tuning BELBIC controllers for a group of linear systems , 2008, 2008 16th Mediterranean Conference on Control and Automation.

[24]  Mohammad Jafari,et al.  Speed control of a Digital Servo System using Brain Emotional Learning Based Intelligent Controller , 2013, 4th Annual International Power Electronics, Drive Systems and Technologies Conference.

[25]  M. Evans,et al.  LETTER TO THE EDITOR: Alternating steady state in one-dimensional flocking , 1998, cond-mat/9811336.

[26]  Ahmed M. El-Garhy,et al.  PSO-BELBIC scheme for two-coupled distillation column process , 2011 .

[27]  Mehrdad Boroushaki,et al.  Load–frequency control of interconnected power system using emotional learning-based intelligent controller , 2012 .

[28]  Reza Javidan,et al.  A novel control strategy for DVR: Optimal bi-objective structure emotional learning , 2016 .

[29]  Caro Lucas,et al.  Real-time embedded emotional controller , 2010, Neural Computing and Applications.

[30]  Gholamreza Arab Markadeh,et al.  Automatic speed control of an asymmetrical six-phase induction motor using emotional controller (BELBIC) , 2014, J. Intell. Fuzzy Syst..